LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea
This addresses the specific need for better readability assessment for L2 English students in Korea, but it is incremental as it builds on existing corpus work.
The paper tackled the problem of low accuracy in readability assessment models for L2 English texts in Korean ELT curricula by improving and expanding the CoKEC-text corpus and training a model, resulting in significantly improved accuracy.
Developing a text readability assessment model specifically for texts in a foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison. In this paper, we investigate a text readability assessment model for L2 English learners in Korea. In accordance, we improve and expand the Text Corpus of the Korean ELT curriculum (CoKEC-text). Each text is labeled with its target grade level. We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.